In a communication network, generally, a telecommunications carrier acquires indices related to communication quality from a communication device and compares them with predetermined threshold values, thereby detecting abnormalities in the operation state of the network. If anomaly is detected, corresponding causes are analyzed, and maintenance is executed by, e.g., changing operation parameters. For example, in a mobile communication network based on 3GPP (3rd Generation Partnership Program) specifications, a radio base station control apparatus should periodically acquire, as communication quality indices for each radio cell, the ratio of the establishment failure count to the establishment attempt count of a radio access bearer, the ratio of the abnormal release count to the establishment success count of a radio access bearer, and the like. These communication quality indices are defined in reference 1 “3GPPTS32.403, “Telecommunication management; Performance Management (PM); Performance measurements-UMTS and combined UMTS/GSM (Release5)”, 2004. [searched May 27, 2005], Internet <URL:http://www.3gpp.org/ftp/Specs/2004-12/Rel-5/32_series/32403-590.zip>”.
These communication quality indices dynamically change depending on the arrangement of the communication network or measurement conditions. Especially, in a mobile communication network such as a portable phone, radio base stations are sometimes installed without forming any void in a planar service area of a region where the communication demand is not necessarily high, thereby increasing the convenience for users. For this reason, traffic greatly depends on the region and time. To detect an anomaly in the communication network, it is necessary not only to appropriately set the threshold values for anomaly determination but also to statistically rely on the individual measured values of communication quality. The threshold values for anomaly determination are often set by an empirical method. Conventional methods of automatically setting appropriate threshold values include adaptive thresholding described in reference 2 “Lucent Technologies, “VitalSQM Service Quality Management Software Brochure”, Nov. 5, 2003 [searched May 27, 2005], Internet <URL:http://www.lucent.com/livelink/09009403800552e4_Brochure_datasheet.pdf>”.
Adaptive thresholding automatically selects appropriate threshold values on the basis of the quality log in the past, instead of permanently setting threshold values for anomaly determination. To exploit the automatic threshold value selection function, the variation in quality log in the past needs to be moderate to some extent, and each measurement result needs to be statistically equally reliable. If these conditions are not satisfied, an anomaly determination error occurs at high probability.
As a conventional statistical anomaly detection system, “deviation value degree calculation device, probability density estimation device used for the device, and forgetting-type histogram calculation device” is described in reference 3 “Japanese Patent Laid-Open No. 2001-101154”. Reference 3 discloses a method of statistically calculating the degree of abnormality of measured data on the basis of the magnitude of a change in probability density distribution obtained by adding the measured data. This method is effective when each measured data has sufficient statistical reliability. However, if data with low statistical reliability exists, an anomaly determination error can occur.
As another conventional anomaly detection system in a communication network, “apparatus for determining communication state in communication network” is disclosed in reference 4 “Japanese Patent Laid-Open No. 10-308824”. Reference 4 describes an anomaly determination method for avoiding the following problem. When connection success ratio calculated from the call connection termination count with respect to the call connection request count per unit time is used as a communication quality index, and the call connection request count is small, an anomaly is determined regardless of high objective quality. Anomaly detection of reference 4 is executed in the following way. Assuming connection success ratio in the normal state of the communication network and that in an abnormal state, one-point likelihood is obtained by using a binomial probability expression on the basis of the set of the measured connection request count and the connection success count. When the likelihood of the abnormal model is much higher than that of the normal model, an anomaly is determined. In reference 4, the statistical reliability of a measured value is taken into consideration in anomaly determination. However, it is also necessary to assume the connection success ratio in the abnormal state in principle. Since the communication network normally operates in most time periods, the connection success ratio in the normal state can relatively easily be assumed. However, it is difficult to assume the connection success ratio in the abnormal state. For this reason, as shown in FIGS. 4 and 5 of reference 4, the determination result largely changes depending on setting of a sensitivity parameter ε. Hence, sensitivity parameter tuning by an expert is necessary.